Metadata-Version: 2.1
Name: vizard
Version: 1.3.0
Summary: Intuitive, Interactive, Easy and Quick Visualizations for Data Science Projects
Home-page: https://github.com/Ritvik19
Author: Ritvik Rastogi
Author-email: rastogiritvik99@gmail.com
License: UNKNOWN
Description: # vizard
        
        Intuitive, Interactive, Easy and Quick Visualizations for Data Science Projects
        
        [![Downloads](https://pepy.tech/badge/vizard)](https://pepy.tech/project/vizard)
        [![Downloads](https://pepy.tech/badge/vizard/month)](https://pepy.tech/project/vizard)
        [![Downloads](https://pepy.tech/badge/vizard/week)](https://pepy.tech/project/vizard)
        
        ## Installation
        
            pip install vizard
        
        or
        
            pip install git+https://github.com/Ritvik19/vizard.git
        
        ## Documentation
        
        ### Instantiate Vizard Object
        
        The Vizard or VizardIn object holds the `DataFrame` along with its configurations including the `PROBLEM_TYPE`, `DEPENDENT_VARIABLE`, `CATEGORICAL_INDEPENDENT_VARIABLES`, `CONTINUOUS_INDEPENDENT_VARIABLES`, and `TEXT_VARIABLES`
        
            import vizard
        
            class config:
                PROBLEM_TYPE = 'regression' or 'classification' or 'unsupervised'
                DEPENDENT_VARIABLE = 'target_variable'
                CATEGORICAL_INDEPENDENT_VARIABLES = [categorical_features]
                CONTINUOUS_INDEPENDENT_VARIABLES = [continuous features]
                TEXT_VARIABLES = [text features]
        
            viz = vizard.Vizard(df, config)
            # for interactive plots use:
            viz = vizard.VizardIn(df, config)
        
        ### Exploratory Data Analysis
        
        After Instatiating the `Vizard` object, you can try different plots for EDA
        
        - Check Missing Values:
        
              viz.check_missing()
        
        - Count of Missing Values:
        
              viz.count_missing()
        
        - Count of Unique Values:
        
              viz.count_unique()
        
        - Count of Missing Values by Group:
        
              viz.count_missing_by_group(class_variable)
        
        - Count of Unique Values by Group:
          viz.count_unique_by_group(class_variable)
        
        ### Target Column Analysis
        
        Based on the type of problem, perform a univariate analysis of target column
        
            viz.dependent_variable()
        
        ### Segmented Univariate Analysis
        
        Based on the type of problem, preform segmented univariate analysis of all feature columns with respect to the target column
        
        - Categorical Variables
        
                viz.categorical_variables()
        
        - Continuous Variables
        
                viz.continuous_variables()
        
        - Text Variables
        
                viz.wordcloud()
        
                viz.wordcloud_by_group()
        
                viz.wordcloud_freq()
        
        ### Bivariate Analysis
        
        Based on the type of variables, perform bivariate analysis on all the feature columns
        
        - Pairwise Scatter
        
                viz.pairwise_scatter()
        
        - Pairwise Violin
        
                viz.pairwise_violin()
        
        - Pairwise Cross Tabs
        
                viz.pairwise_crosstabs()
        
        ### Trivariate Analysis
        
        Based on the type of variables, perform trivariate analysis on any of the feature columns
        
        - Trivariate Bubble (Continuous vs Continuous vs Continuous)
        
                viz.trivariate_bubble(x, y, s)
        
        - Trivariate Scatter (Continuous vs Continuous vs Categorical)
        
                viz.trivariate_scatter(x, y, c)
        
        - Trivariate Violin (Categorical vs Continuous vs Categorical)
        
                viz.trivariate_violin(x, y, c)
        
        ### Correlation Analysis
        
        Based on the type of variables, perform correaltion analysis on all the feature columns
        
        - Correlation Plot
        
                viz.corr_plot()
        
        - Pair Plot
        
                viz.pair_plot()
        
        - Chi Square Plot
        
                viz.chi_sq_plot()
        
        ## Save the plots to PDF using Viz2PDF
        
        You can also save the plots to a pdf file in order to generate an EDA report
        
        The `Viz2PDF` object takes in all your `Vizard` plots and creates a pdf report out of them
        
        ```
        viz = vizard.Vizard(df, config)
        viz2pdf = vizard.Viz2PDF('viz_report.pdf')
        
        plots = [
            viz.check_missing(),
            viz.count_missing(),
            viz.count_unique(),
            viz.dependent_variable(),
            viz.categorical_variables(),
            viz.continuous_variables(),
            viz.pairwise_scatter(),
            viz.pairwise_violin(),
            viz.pairwise_crosstabs(),
        ]
        viz2pdf(plots)
        ```
        
        ## Usage
        
        1. [Classification Case](https://nbviewer.jupyter.org/github/Ritvik19/vizard-doc/blob/main/usage/Classification%20Case.ipynb)
        2. [Regression Case](https://nbviewer.jupyter.org/github/Ritvik19/vizard-doc/blob/main/usage/Regression%20Case.ipynb)
        3. [Text Classification Case](https://nbviewer.jupyter.org/github/Ritvik19/vizard-doc/blob/main/usage/Text%20Classification%20Case.ipynb)
        4. [Unsupervised Case](https://nbviewer.jupyter.org/github/Ritvik19/vizard-doc/blob/main/usage/Unsupervised%20Case.ipynb)
        5. [Classification Case (Interactive)](https://nbviewer.jupyter.org/github/Ritvik19/vizard-doc/blob/main/usage/Classification%20Interactive%20Case.ipynb)
        6. [Regression Case (Interactive)](https://nbviewer.jupyter.org/github/Ritvik19/vizard-doc/blob/main/usage/Regression%20Interactive%20Case.ipynb)
        7. [Unsupervised Case (Interactive)](https://nbviewer.jupyter.org/github/Ritvik19/vizard-doc/blob/main/usage/Unsupervised%20Interactive%20Case.ipynb)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
